Promising Quantum Computing Applications in Aerospace

Quantum computing has received significant attention recently, and new breakthroughs have recently permitted operational devices to demonstrate functionality. Unlike classical devices, quantum computers leverage quantum-mechanical properties of superposition, entanglement and interference to permit new algorithms to be constructed that may provide significant speed-ups over existing classical approaches for key problems. Near-term devices are characterized as noisy, intermediate-scale quantum (NISQ) devices, for which several hybrid classical/quantum algorithms, such as variational quantum eigensolver (VQE) and quantum alternating-operator ansatz (QAOA), have been introduced to take advantage of near-term systems.

Several key areas within aerospace stand to benefit from quantum computing. Areas such as corrosion damage estimation and modeling, aircraft tail assignment optimization, and ply composites stacking sequence optimization have recently been explored using NISQ devices to attempt solving various forms of these problems. In the case of corrosion damage, quantum simulation to model electronic systems of corrosive chemical reactions for aerospace metals such as Magnesium and Aluminum could enable a superior understanding of corrosion damage accumulation over time and aircraft lifetime estimation. Hybrid approaches combining both density functional theory (DFT) and quantum simulation have also been explored to enable near-term investigations using current quantum hardware. In the cases of combinatorial optimization, the use of quantum annealing, VQE, and QAOA have been explored for applications such as tail assignment and ply composites. This presentation shall introduce these aerospace applications and discuss the utility and value that quantum-enabled speed-ups would permit. Results for exploring the use of NISQ devices to solve these various problems will be presented, and current and remaining challenges with leveraging true speed-ups shall be highlighted. Future algorithms that may be enabled with superior hardware and future application areas will also be discussed.